808 research outputs found

    Interpretation of UV Absorption Lines in SN1006

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    We present a theoretical interpretation of the broad silicon and iron UV absorption features observed with the Hubble Space Telescope in the spectrum of the Schweizer-Middleditch star behind the remnant of Supernova 1006. These features are caused by supernova ejecta in SN1006. We propose that the redshifted SiII2 1260 A feature consists of both unshocked and shocked SiII. The sharp red edge of the line at 7070 km/s indicates the position of the reverse shock, while its Gaussian blue edge reveals shocked Si with a mean velocity of 5050 km/s and a dispersion of 1240 km/s, implying a reverse shock velocity of 2860 km/s. The measured velocities satisfy the energy jump condition for a strong shock, provided that all the shock energy goes into ions, with little or no collisionless heating of electrons. The line profiles of the SiIII and SiIV absorption features indicate that they arise mostly from shocked Si. The total mass of shocked and unshocked Si inferred from the SiII, SiIII and SiIV profiles is M_Si = 0.25 \pm 0.01 Msun on the assumption of spherical symmetry. Unshocked Si extends upwards from 5600 km/s. Although there appears to be some Fe mixed with the Si at lower velocities < 7070 km/s, the absence of FeII absorption with the same profile as the shocked SiII suggests little Fe mixed with Si at higher (before being shocked) velocities. The column density of shocked SiII is close to that expected for SiII undergoing steady state collisional ionization behind the reverse shock, provided that the electron to SiII ratio is low, from which we infer that most of the shocked Si is likely to be of a fairly high degree of purity, unmixed with other elements. We propose that the ambient interstellar density on the far side of SN1006 is anomalously low compared to the density around the rest of the remnant. ThisComment: 24 pages, with 8 figures included. Accepted for publication in the Astrophysical Journa

    RCytoscape: Tools for Exploratory Network Analysis

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    Background: Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail, and ill-equipped to explore the variety and quantities of data available today, and the complex causes we seek to understand. Results: RCytoscape integrates R (an open-ended programming environment rich in statistical power and datahandling facilities) and Cytoscape (powerful network visualization and analysis software). RCytoscape extends Cytoscape\u27s functionality beyond what is possible with the Cytoscape graphical user interface. To illustrate the power of RCytoscape, a portion of the Glioblastoma multiforme (GBM) data set from the Cancer Genome Atlas (TCGA) is examined. Network visualization reveals previously unreported patterns in the data suggesting heterogeneous signaling mechanisms active in GBM Proneural tumors, with possible clinical relevance. Conclusions: Progress in bioinformatics and computational biology depends upon exploratory and confirmatory data analysis, upon inference, and upon modeling. These activities will eventually permit the prediction and control of complex biological systems. Network visualizations -- molecular maps -- created from an open-ended programming environment rich in statistical power and data-handling facilities, such as RCytoscape, will play an essential role in this progression

    Π‘ΠžΠ¦Π˜ΠΠ›Π¬ΠΠž-Π­ΠšΠžΠΠžΠœΠ˜Π§Π•Π‘ΠšΠ˜Π• И ΠŸΠžΠ›Π˜Π’Π˜ΠšΠž-ΠŸΠ ΠΠ’ΠžΠ’Π«Π• ΠΠ‘ΠŸΠ•ΠšΠ’Π« ΠŸΠžΠ‘Π’ΠšΠ Π˜Π—Π˜Π‘ΠΠžΠ™ ΠœΠ˜Π“Π ΠΠ¦Π˜Π˜

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    The paper deals with the socio-economic and political-legal aspects of the post-crisis migration over time between 2012 and the first half of 2016 based on the data of the Automated Analytical Reporting System (AARS) of the Federal Migration Service, the State Statistical Records of the Federal State Statistics Service, statistical information of the General Directorate for Migration of the RF Interior Ministry. The purpose of the study was to analyze the factors of the migration processes development in problem regions of the post-Soviet space as well as the aftershock problems of the secondary migration from European countries that have to solve the problems of the mass flow of migrants from regions of armed and political conflicts. To achieve the goal, the author posed the following tasks: 1) the review of labor, capital, financial and other resources of the migration donor regions in the context of optimizing management decisions on the regulation of migration processes over the territory of the Russian Federation with a focus on individual economic sectors and occupational skill characteristics; 2) the study of migration processes in the labor market in accordance with indices established by the Russian Rules for Labor Market Monitoring; 3) the study of the migration activity in the DPRK and the PRC compared with political and legal decisions of local and central authorities of the Russian Federation in demographically unstable regions of the Far East and Siberia; 4) assessing the prospects for Russian investments in migration donor countries to level migration flows on financial and economic conditions favorable for the recipient country; 5) systematization of mechanisms for managing the goal setting for migration flows and attracting foreign workers in priority occupational skill groups in line with the Russian economy demands and the public consent interests. Based on the task solution results, it is intended to develop a mid-term forecast of external migration risks for the Russian Federation and propose a system of measures to prevent the migration threats of the post-crisis migration.Π’ ΡΡ‚Π°Ρ‚ΡŒΠ΅ Ρ€Π°ΡΡΠΌΠ°Ρ‚Ρ€ΠΈΠ²Π°ΡŽΡ‚ΡΡ ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎ-экономичСскиС ΠΈ ΠΏΠΎΠ»ΠΈΡ‚ΠΈΠΊΠΎ-ΠΏΡ€Π°Π²ΠΎΠ²Ρ‹Π΅ аспСкты посткризисной ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΈ Π² Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ΅ с 2012 Π³. Π΄ΠΎ ΠΏΠ΅Ρ€Π²ΠΎΠΉ ΠΏΠΎΠ»ΠΎΠ²ΠΈΠ½Ρ‹ 2016 Π³. Π½Π° основС Π΄Π°Π½Π½Ρ‹Ρ… Автоматизированной систСмы аналитичСской отчСтности Π€Π΅Π΄Π΅Ρ€Π°Π»ΡŒΠ½ΠΎΠΉ ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠΉ слуТбы (АБАО), ГосударствСнной статистичСской отчСтности Росстата, статистичСской ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ Π“Π»Π°Π²Π½ΠΎΠ³ΠΎ управлСния ΠΏΠΎ вопросам ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΈ ΠœΠΈΠ½ΠΈΡΡ‚Π΅Ρ€ΡΡ‚Π²Π° Π²Π½ΡƒΡ‚Ρ€Π΅Π½Π½ΠΈΡ… Π΄Π΅Π» Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ. ЦСль исслСдования - Π°Π½Π°Π»ΠΈΠ· Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ² развития ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… процСссов Π² ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΠ½Ρ‹Ρ… Ρ€Π΅Π³ΠΈΠΎΠ½Π°Ρ… постсовСтского пространства, ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹ Π°Ρ„Ρ‚Π΅Ρ€ΡˆΠΎΠΊΠ° Π²Ρ‚ΠΎΡ€ΠΈΡ‡Π½ΠΎΠΉ ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΈ ΠΈΠ· стран Π•Π²Ρ€ΠΎΠΏΡ‹, Π²Ρ‹Π½ΡƒΠΆΠ΄Π΅Π½Π½Ρ‹Ρ… Ρ€Π΅ΡˆΠ°Ρ‚ΡŒ ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹ массового ΠΏΠΎΡ‚ΠΎΠΊΠ° ΠΌΠΈΠ³Ρ€Π°Π½Ρ‚ΠΎΠ² ΠΈΠ· Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² Π²ΠΎΠΎΡ€ΡƒΠΆΠ΅Π½Π½Ρ‹Ρ… ΠΈ политичСских ΠΊΠΎΠ½Ρ„Π»ΠΈΠΊΡ‚ΠΎΠ². Для достиТСния Ρ†Π΅Π»ΠΈ Π°Π²Ρ‚ΠΎΡ€ поставил ΡΠ»Π΅Π΄ΡƒΡŽΡ‰ΠΈΠ΅ Π·Π°Π΄Π°Ρ‡ΠΈ: 1) рассмотрСниС ΠΏΠΎΡ‚Π΅Π½Ρ†ΠΈΠ°Π»Π° Ρ‚Ρ€ΡƒΠ΄ΠΎΠ²Ρ‹Ρ…, ΠΊΠ°ΠΏΠΈΡ‚Π°Π»ΡŒΠ½Ρ‹Ρ…, финансовых, Π΄Ρ€ΡƒΠ³ΠΈΡ… рСсурсов Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² - ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… Π΄ΠΎΠ½ΠΎΡ€ΠΎΠ² Π² контСкстС ΠΎΠΏΡ‚ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΈ управлСнчСских Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ ΠΏΠΎ Ρ€Π΅Π³ΡƒΠ»ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΡŽ ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… процСссов Π½Π° Ρ‚Π΅Ρ€Ρ€ΠΈΡ‚ΠΎΡ€ΠΈΠΈ Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ, Π² Ρ‚ΠΎΠΌ числС Π² Ρ€Π°Π·Ρ€Π΅Π·Π΅ ΠΎΡ‚Π΄Π΅Π»ΡŒΠ½Ρ‹Ρ… отраслСй экономики, ΠΏΡ€ΠΎΡ„Π΅ΡΡΠΈΠΎΠ½Π°Π»ΡŒΠ½ΠΎ-ΠΊΠ²Π°Π»ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… характСристик; 2) ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠ΅ ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… процСссов Π½Π° Ρ€Ρ‹Π½ΠΊΠ΅ Ρ‚Ρ€ΡƒΠ΄Π° согласно показатСлям, установлСнным ΠŸΡ€Π°Π²ΠΈΠ»Π°ΠΌΠΈ провСдСния ΠΌΠΎΠ½ΠΈΡ‚ΠΎΡ€ΠΈΠ½Π³Π° ситуации Π½Π° Ρ€Ρ‹Π½ΠΊΠ΅ Ρ‚Ρ€ΡƒΠ΄Π° Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ; 3) исслСдованиС ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠΉ активности ΠšΠΠ”Π , КНР Π² сопоставлСнии с ΠΏΠΎΠ»ΠΈΡ‚ΠΈΠΊΠΎ-ΠΏΡ€Π°Π²ΠΎΠ²Ρ‹ΠΌΠΈ Ρ€Π΅ΡˆΠ΅Π½ΠΈΡΠΌΠΈ мСстной ΠΈ Ρ†Π΅Π½Ρ‚Ρ€Π°Π»ΡŒΠ½ΠΎΠΉ власти Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ Π² дСмографичСски нСустойчивых Ρ€Π΅Π³ΠΈΠΎΠ½Π°Ρ… Π”Π°Π»ΡŒΠ½Π΅Π³ΠΎ Востока, Π‘ΠΈΠ±ΠΈΡ€ΠΈ; 4) выяснСниС пСрспСктив российского инвСстирования Π² страны, ΡΠ²Π»ΡΡŽΡ‰ΠΈΠ΅ΡΡ ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹ΠΌΠΈ Π΄ΠΎΠ½ΠΎΡ€Π°ΠΌΠΈ для нивСлирования ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… ΠΏΠΎΡ‚ΠΎΠΊΠΎΠ² Π½Π° Π²Ρ‹Π³ΠΎΠ΄Π½Ρ‹Ρ… для страны Ρ€Π΅Ρ†ΠΈΠΏΠΈΠ΅Π½Ρ‚Π° финансово-экономичСских условиях; 5) систСматизация ΠΌΠ΅Ρ…Π°Π½ΠΈΠ·ΠΌΠΎΠ² управлСния Ρ†Π΅Π»Π΅ΠΏΠΎΠ»Π°Π³Π°Π½ΠΈΠ΅ΠΌ ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… ΠΏΠΎΡ‚ΠΎΠΊΠΎΠ² ΠΈ ΠΏΡ€ΠΈΠ²Π»Π΅Ρ‡Π΅Π½ΠΈΠ΅ иностранных Ρ€Π°Π±ΠΎΡ‚Π½ΠΈΠΊΠΎΠ² ΠΏΠΎ ΠΏΡ€ΠΈΠΎΡ€ΠΈΡ‚Π΅Ρ‚Π½Ρ‹ΠΌ ΠΏΡ€ΠΎΡ„Π΅ΡΡΠΈΠΎΠ½Π°Π»ΡŒΠ½ΠΎ-ΠΊΠ²Π°Π»ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΎΠ½Π½Ρ‹ΠΌ Π³Ρ€ΡƒΠΏΠΏΠ°ΠΌ Π² соотвСтствии со спросом российской экономики ΠΈ интСрСсами общСствСнного согласия. Π’ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π΅ Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ поставлСнных Π·Π°Π΄Π°Ρ‡ прСдполагаСтся ΡΡ„ΠΎΡ€ΠΌΡƒΠ»ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ срСднСсрочный ΠΏΡ€ΠΎΠ³Π½ΠΎΠ· Π²Π½Π΅ΡˆΠ½ΠΈΡ… ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… рисков для Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ ΠΈ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠΈΡ‚ΡŒ систСму ΠΌΠ΅Ρ€ ΠΏΠΎ ΠΏΡ€Π΅Π²Π΅Π½Ρ‚ΠΈΠ·Π°Ρ†ΠΈΠΈ ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… ΡƒΠ³Ρ€ΠΎΠ· посткризисной ΠΌΠΈΠ³Ρ€Π°Ρ†ΠΈΠΈ
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